Non-emergency municipal services, e.g., city 311 systems, have been widely implemented across cities in Canada and the United States to enhance residents' quality of life. These systems enable residents to report issues, e.g., noise complaints, missed garbage collection, and potholes, via phone calls, mobile applications, or webpages. However, residents are often given limited information about when their service requests will be addressed, which can reduce transparency, lower resident satisfaction, and increase the number of follow-up inquiries. Predicting the service time for municipal service requests is challenging due to several complex factors: (i) dynamic spatial-temporal correlations, (ii) underlying interactions among heterogeneous service request types, and (iii) high variation in service duration even within the same request category. In this work, we propose MuST2-Learn: a Multi-view Spatial-Temporal-Type Learning framework designed to address the aforementioned challenges by jointly modeling spatial, temporal, and service type dimensions. In detail, it incorporates an inter-type encoder to capture relationships among heterogeneous service request types and an intra-type variation encoder to model service time variation within homogeneous types. In addition, a spatiotemporal encoder is integrated to capture spatial and temporal correlations in each request type. The proposed framework is evaluated with extensive experiments using two real-world datasets. The results show that MuST2-Learn reduces mean absolute error by at least 32.5%, which outperforms state-of-the-art methods.

Speaker diarization refers to identifying who speaks what in a conversation. It is critical in sensitive settings like psychological counseling and legal consultations. However, traditional approaches, such as microphone or video, raise privacy concerns and cause discomfort to participants due to their noticeable deployment. To address this, we propose a non-intrusive speaker diarization system via mmWave sensing. Our approach leverages the spatial diversity of signals from multiple objects to distinguish speakers. Specifically, it isolates speech-induced vibrating objects signals and extracts speaker-related features through a two-stage feature extraction process. Our system achieves over 93% accuracy in real-world scenarios, demonstrating its effectiveness in reliably distinguishing speakers.

Advanced wireless communication systems adopt deep learning (DL) approaches to achieve automatic modulation recognition (AMR) for spectrum monitoring and management, especially in the spectrum bands supporting diverse co-existing wireless protocols. In practical wireless environments, wireless signals can easily get compromised by malicious noise, intentional interference, and adversarial attacks, reducing the effectiveness of AMR. By exploiting DL model vulnerabilities, an undetectable perturbation added to the wireless signal can cause misclassification, resutling in serious consequences including decoding errors, throughput degradation, and communication disruption. Facing the limitations of existing works on defending against wireless adversarial attacks, this work innovates the Transformer model to design an adversarial robust AMR driven by exploring temporal correlation in time-sequence wireless signals. Instead of directly applying the Vision Transformer (ViT), we first innovate a feature extraction module specifically for radio frequency (RF) signals from both the time and frequency domains, together with an adaptive positional embedding to the Transformer encoder for enhancing AMR accuracy. To mitigate the noise effect in practical wireless communication, we then propose a noise-adaptive adversarial training scheme on the developed Transformer-based model using adversarial examples crafted by white-box attackers. To show the scheme's efficiency, effectiveness, and robustness, our proposed design has been thoroughly evaluated via a self-collected real-world dataset consisting of over 30 million wireless signal data samples with 21 modulation schemes in both indoor and outdoor scenarios. Our results reach a maximum accuracy of 94.17% in AMR classification and 71.2 % under adversarial attacks. Besides, for the first time, we demonstrate the robustness of our design under a real wireless adversarial attack in real-time. Datasets and code available in https://github.com/coulsonlee/Robust-ViT-for-AMR-SP2025.

Memory Electromagnetic Radiation (EMR) allows attackers to manipulate the DRAM of infiltrated systems to leak sensitive secret information. Although most of the existing works have demonstrated its feasibility, practical concerns, such as the ideal electromagnetic environment and stationary attacking layout, make the covert channel attack less convincing, especially in vulnerable sites such as offices and data centers. This work removes the above impractical assumptions to uncover the potential of memory EMR by proposing the first parallel EMR covert communication protocol. Our design reshapes the current "1-to-1" covert communication mode to "n-to-1" mode via a novel pattern-based 2-dimensional symbol encoding scheme, allowing multiple victim computers to simultaneously perform data exfiltration to one attacker (the receiver) without mutual interference. Meanwhile, this novel scheme design also enables the very first mobile attacker, i.e., a smartphone connected to a software-defined radio (SDR) dongle, to capture parallel memory EMR signals in a volatile environment. Extensive experiments are conducted to verify the performance in a volatile environment with different parameter configurations, distances, motion modes, shielding materials, orientations, hardware configurations, and SDR platforms. Our experimental results demonstrate that FreeEM can support up to 4 parallel memory EMR transmissions to achieve an overall throughput of 625Kbps and a decoding accuracy of 96.88%. The maximum communication distance can reach up to 20 meters.

Class imbalance is a well-recognized challenge in GNN-based fraud detection. Traditional methods like re-sampling and re-weighting address this issue by balancing class distribution. However, node class balancing with simple re-sampling or re-weighting may greatly distort the data distributions and eventually lead to the ineffective performance of GNNs. In this paper, we propose a novel approach named Graph Generative Node Augmentation (GGA), which improves GNN-based fraud detection models by augmenting synthetic nodes of the minority class. GGA utilizes the GAN framework to synthesize node features and related edges of fake fraudulent nodes. To introduce greater variety in the generated nodes, we employ an MLP for feature generation. We also introduce an attention module to encode feature-level information before graph convolutional layers for edge generation. Our empirical results on two real-world fraud datasets demonstrate that GGA improves the performance of GNN-based fraud detection models by a large margin with much fewer nodes than traditional class balance methods, and outperforms recent graph augmentation methods with the same number of synthetic nodes.

Future mobile edge computing (MEC) is envisioned to provide federated intelligence to delay-sensitive learning tasks with multimodal data. Conventional horizontal federated learning (FL) suffers from high resource demand in response to complicated multi-modal models. Multi-modal FL (MFL), on the other hand, offers a more efficient approach for learning from multi-modal data. In MFL, the entire multi-modal model is split into several sub-models with each tailored to a specific data modality and trained on a designated edge. As sub-models are considerably smaller than the multi-modal model, MFL requires fewer computation resources and reduces communication time. Nevertheless, deploying MFL over MEC faces the challenges of device mobility and edge heterogeneity, which, if not addressed, could negatively impact MFL performance. In this paper, we investigate an Service Migration-assisted Mobile Multi-modal Federated Learning (SM3FL) framework, where the service migration for sub-models between edges is enabled. To effectively utilize both communication and computation resources without extravagance in SM3FL, we develop the optimal strategies of service migration and data sample collection to minimize the wall-clock time, defined as the required training time to reach the learning target. Our experiment results show that the proposed SM3FL framework demonstrates remarkable performance, surpassing other state-of-art FL frameworks via substantially reducing the computing demand by 17.5% and dramatically decreasing the wall-clock time by 25.3%.

Internet of Things (IoT) is transforming every corner of our daily life and plays important roles in the smart home. Depending on different requirements on wireless transmission, dedicated wireless protocols have been adopted on various types of IoT devices. Recent advances in Cross-Technology Communication (CTC) enable direct communication across those wireless protocols, which will greatly improve the spectrum utilization efficiency. However, it incurs serious security concerns on heterogeneous IoT devices. In this paper, we identify a new physical-layer attack, cross-technology signal emulation attack, where a WiFi device eavesdrops a ZigBee packet on the fly, and further manipulates the ZigBee device by emulating a ZigBee signal. To defend against this attack, we propose two defense strategies with the help of a commonly found WiFi router. Particularly, the passive defense strategy focuses on misleading the ZigBee signal eavesdropping, while the proactive approach develops a real-time detection mechanism on distinguishing between a common ZigBee signal and an emulated signal. We implement the complete attacking process and defense strategies with TI CC26x2R LaunchPad, USRP-N210 platform, and a self-designed prototype. Extensive experiments have demonstrated the existence of the attack, and the feasibility, effectiveness, and accuracy of the proposed defense strategies.

Intelligent reflecting surface (IRS) is considered as an effective technology to enhance the performance of wireless communication systems. In this paper, the robust optimization design of the IRS-assisted wireless multi-group multicast MISO system with statistical CSI errors is investigated. Two optimization problems, namely max-min fairness problem and QoS problem, are discussed separately. In order to deal with the non-convex imperfect CSI constraint, the Bernstein-type inequality is utilized to transform the outage probability constraint into a second-order cone (SOC) constraint and linear inequalities. Furthermore, two efficient algorithms based on alternating optimization (AD) are proposed to solve the reformulated problems, respectively. In particular, the semi-definite relaxation (SDR) technique is applied to optimize the transmit beamforming and IRS reflection coefficients. The numerical simulation results indicate that by deploying IRS and utilizing the proposed algorithms, the system performance can be improved significantly. However, the gain of introducing IRS in the system heavily depends on the bound of the CSI error.

Information sharing through online social networks (OSNs) facilitates quick discovery and consumption of information online. Many OSNs such as Facebook, Twitter provide resharing or reposting features, which allows users to share others' content with their own friends or followers. As content is shared from person to person, cascades of information-sharing can occur. There are many existing works focusing on analyzing and characterizing the cascades in OSNs. However, previous works focus on the analysis and characterization of cascades without providing a solution to accurately predict cascades. Although some methods for cascade prediction have been proposed recently, their methods work in social networks such as Facebook (or Twitter), and do not work well simultaneously in multiple OSNs such as Software Social Network (SSN) GitHub, Twitter and Reddit because GitHub, Twitter and Reddit have different social activity patterns. In this paper, we first perform a thorough analysis of cascades in multiple OSNs: GitHub, Twitter and Reddit, and identify the cascades of information-sharing. We then propose CrossCas, a novel cross-platform approach for predicting cascades in multiple OSNs with Hidden Markov Model (HMM). The experimental results show that our proposed method achieves high performance.

The wide deployment of IoT devices has resulted in a critical shortage of spectrum resources. Many IoT devices coexist on the same spectrum band, where the network performance is always degraded. As a promising solution, the Cross-Technology Communication (CTC) enables the direct communication among heterogeneous IoT devices. Unfortunately, the emerging cross-technology attacks have demonstrated their high success rates in terms of spoofing the end IoT devices or jamming the communication channels. In this paper, we investigate a novel cross-technology jamming issue for a distributed heterogeneous IoT system. Compared with traditional jamming methods, the cross-technology jammer has a much higher jamming power, wider jamming bandwidth, and stronger stealthiness, all of which deserve a complete re-thinking of defensive mechanisms. Therefore, we propose a hybrid anti-jamming scheme that jointly considers frequency hopping and power control techniques. Specifically, we model the anti-jamming process as a Markov Decision Process (MDP) and adopt Deep Q-Network (DQN) to find the optimal strategy. Extensive real-world experiments show that the goodput (payload data) of our anti-jamming scheme can achieve up to 2X and 1.39X than the passive and random anti-jamming approaches, respectively. In particular, our anti-jamming scheme provides 78% of goodput with the presence of a cross-technology jammer, outperforming existing passive and random anti-jamming scheme designs at 37.6% and 54.1%.

The proliferation of Internet of Things (IoT) has transformed the way people interact with the world. Various kinds of wireless protocols have been developed to support diverse types of IoT communications. Unfortunately, the lack of spectrum resources puts a hard limit on managing the large-scale heterogeneous IoT system. Although previous works alleviate this strain by coordinating transmission power, time slots, and sub-channels, they may not be feasible in future IoT applications with dense deployments. In this paper, we explore a physical-level parallel inclusive communication paradigm for the coexistence of Wi-Fi and ZigBee, which leverages novel bits embedding approaches on the OQPSK protocol to enable both Wi-Fi and ZigBee IoT devices to decode the same inclusive signals at the same time but with each one’s different data. By carefully crafting the inclusive signals using legacy Wi-Fi protocol, the overlapping spectrum can be simultaneously re-used by both protocols, expecting a maximum data rate (250kbps) for ZigBee devices and up to 3.75Mbps for a Wi-Fi pair over only a 2MHz bandwidth. The achieved spectrum efficiency outperforms a majority of CTC schemes and parallel communication designs. Compared with existing works on parallel communication, our proposed system is the first one that achieves an entire software-level design, which can be readily implemented on Commercial Off-The-Shelf (COTS) devices without any hardware modification. Based on extensive real-world experiments on both USRP and COTS device platforms, we demonstrate the feasibility, generality, and efficiency of the proposed new paradigm.

Abstract The hybrid visible light communication (VLC)/radio frequency (RF) communications are investigated with the aid of reconfigurable intelligent surfaces (RISs) in dynamic wireless networks, where the RIS access selection processes of VLC/RF users are updated depending on the channel quality dynamically. Specifically, a dynamic optimization problem due to the mobility of users and time‐varying selection strategy is formulated. Under the constraints of the average minimum rate for VLC/RF users and the maximum transmit power constraints for VLC/RF access points (APs), the target is to minimize the average long‐term power consumption, by jointly considering the active beamforming at APs and the passive beamforming at RISs. Based on the Lyapunov optimization framework and the drift‐plus‐penalty (DPP) algorithm, the original optimization problem is transformed into corresponding short‐term problems at each frame. Furthermore, the closed form solutions with active‐passive beamforming are derived using the fractional programming method based on the Lagrangian dual theory. Finally, numerical results demonstrate the convergence and effectiveness of the proposed optimization algorithm.

The increasing deployment of wireless sensors enables a broad spectrum of health-related wearable applications. Due to the sensitivity of collected personal health information, these wearables should be authenticated together with their users as “wearable-user pairs” to ensure that they are attached to legitimate users. However, various devices are equipped with dedicated sensing abilities and wireless protocols corresponding to data characteristics in practice. Traditional authentication methodologies may not work in this heterogeneous environment because of protocol incompatibility. For example, how to verify a new ZigBee-enabled monitor when the existing trusted device is Wi-Fi-enabled? Therefore, to achieve authentication across protocols, in this article, we leverage the unique cross-technology interference (CTI), triggered by heterogeneous wireless transmissions, along with human physiological activity measurements (e.g., respiration patterns) to design an authentication scheme between wearables and users. Specifically, the authentication from an unknown ZigBee wearable to a trusted Wi-Fi device is achieved by monitoring the channel state information (CSI) changes according to human respiration. Our approach not only successfully recognizes a legitimate wearable-user pair but also blocks illegal access from adversaries. Extensive experiments have been conducted to demonstrate both the security and feasibility of the proposed scheme. The designed mechanism can achieve over 92% authentication accuracy with human subjects.

The non-intrusive human activity recognition has been envisioned as a key enabler for many emerging applications requiring interactions between humans and computing systems. To accurately recognize different human behaviors, ubiquitous wireless signals are widely adopted, e.g., Wi-Fi signals, whose Channel State Information (CSI) can precisely reflect human movements. Unfortunately, nearly all Wi-Fi-based recognition systems assume a clean wireless environment, i.e., no interference will compromise the developed algorithms, which, apparently, is not feasible in practice. Even worse, for systems using Wi-Fi 2.4GHz signals, the widely existing interference from coexisting protocols, such as ZigBee, Bluetooth, and LTE-Unlicensed, can easily compromise the recognition process, posing a hard limit on further enhancing the accuracy. Therefore, this work uncovers a new signal adversarial attack against Wi-Fi-based human activity recognition systems, by intentionally injecting interference using coexisting protocol signals. The contaminated Wi-Fi signal will distort CSI estimation and finally output a false recognition result. Different from traditional jamming attacks, this new adversarial attack is intelligent and stealthy in terms of avoiding being detected from traffic analysis. Along with both theoretical analysis and extensive real-world experiments, we have shown this newly-identified attack can easily compromise many existing Wi-Fi-based human recognition systems while still bypassing existing schemes for malicious signal detection.

Mobile crowd sensing is a technique where a crowd sensing server outsources sensing tasks to the crowd for mobile data collection. In mobile crowd sensing, some tasks require location information to achieve their objectives, such as road monitoring, indoor floor plan reconstruction, and smart transportation. This required information incurs severe concerns on location privacy leakage and threatens workers' properties as well as public safety. In some cases, even sensing data itself can be used as auxiliary information resulting in location privacy breaches. Many existing works apply differential privacy mechanisms for location privacy preservation to tackle this problem, but they cannot efficiently fulfill privacy goals because each worker only considers his own privacy. As a consequence, the accumulated privacy budget will lower down the composed privacy level of all the workers' locations. In addition, deploying differential privacy is costly for workers and it will degrade the quality of data required in crowd sensing tasks. How to balance the cost and provide accurate aggregated data while fulfilling privacy objectives becomes a challenging issue. In this paper, we propose a group-differentially-private game-theoretical solution, which addresses these limitations in a privacy-preserving and efficient way. Our scheme enables the indistinguishability of workers' locations and sensing data without the help of a trusted entity while meeting the accuracy demands of crowd sensing tasks. The effectiveness and efficiency of our scheme are thoroughly evaluated based on real-world datasets.

Widely deployed IoT devices have raised serious concerns for the spectrum shortage and the cost of multi-protocol gateway deployment. Recent emerging Cross-Technology Communication (CTC) technique can alleviate this issue by enabling direct communication among heterogeneous wireless devices, such as WiFi, Bluetooth, and ZigBee on 2.4 GHz. However, this new paradigm also brings security risks, where an attacker can use CTC to launch wireless attacks against IoT devices. Due to limited computational capability and different wireless protocols being used, many IoT devices are unable to use computationally-intensive cryptographic approaches for security enhancement. Therefore, without proper detection methods, IoT devices cannot distinguish signal sources before executing command signals. In this paper, we first demonstrate a new defined physical layer attack in the CTC scenario, named as waveform emulation attack, where a WiFi device can overhear and emulate the ZigBee waveform to attack ZigBee IoT devices. Then, to defend against this new attack, we propose a physical layer defensive mechanism, named as AuthCTC, to verify the legitimacy of CTC signals. Specifically, at the sender side, an authorization code is embedded into the packet preamble by leveraging the dynamically changed cyclic prefix. A WiFi-based detector is used to verify the authorization code at the receiver side. Extensive simulations and experiments using off-the-shelf devices are conducted to demonstrate both the feasibility of the attack and the effectiveness of our defensive mechanism.

The exponentially increasing number of heterogeneous Internet of Things (IoT) devices result in severe spectrum shortage and interference in the already crowded ISM band. Cross-Technology Communication (CTC) is dedicated to achieving direct communication among wireless devices with different radios and modulation schemes, which serves as an effective approach to address the above challenges. Nevertheless, CTC also provides opportunities for adversaries to manipulate IoT devices. In this paper, we identify a new attack. Built on CTC, WiFi devices are able to hide the pre-intercepted ZigBee message into their transmitted waveforms, achieving the objective of directly controlling ZigBee devices. To defend against the attack, we analyze possible strategies and consider constellation higher-order statistic analysis as the countermeasure. Extensive simulations and experiments with commodity devices (CC26x2R1) and USRP-based prototypes show the existence of the newly identified attack, and further, validate the effectiveness of the proposed defensive approach.

Internet of Things (IoT) has emerged as a new computing paradigm that promises to offer a fully connected “smart” world. However, due to the open nature of wireless medium, the information sensed, collected, and transmitted by IoT devices can be easily intercepted by adversaries, which becomes a serious concern in most IoT applications requiring sensitive data. In practice, cooperative communication approaches can effectively improve the security level for wireless communication under the presence of eavesdroppers with unbounded computational ability. In this paper, we apply the amplify-and-forward (AF) cooperative communication to increase the secrecy capacity of IoT systems by incentivizing relay IoT devices. Specifically, a Stackelberg game is designed to motivate the participation of the relay IoT devices for security enhancement. Extensive experimental results have demonstrated the feasibility and security of the proposed mechanism under both unknown and known channel state information (CSI) models.

The exploding popularity of mobile devices enables people to enjoy the benefits brought by various interesting mobile apps. The ever-increasing data traffic has exacerbated energy consumption on both cellular service providers and mobile users. It has become an urgent need to reducing the energy consumption in the cellular network while satisfying users' increasing traffic demands. Mobile data offloading is an effective energy-saving paradigm to tackle the above-mentioned problem. However, the current approaches cannot fully address the issue in terms of user demands and offloaded traffic. With the observation that duplicated data transmission often happens in the crowd with similar social interests, we deploy device-to-device (D2D) data offloading to achieve the energy efficiency at the user side while adapting their increasing traffic demands. Specifically, we investigate the stochastic optimization of the long-term time-averaged expected energy consumption while guaranteeing the strong stability of the network by utilizing the social-aware and energy-efficient D2D mobile offloading. By jointly considering interference among D2D users, social-aware caching, link scheduling, and routing, an offline finite-queue-aware energy minimization problem is formulated, which is a time-coupling stochastic mixed-integer non-linear programming (MINLP) problem. We propose an online finite-queue-aware energy algorithm by employing the Lyapunov drift-plus-penalty theory. Extensive analysis and simulations are conducted to validate the proposed scheme.

Dynamic Spectrum Access (DSA) has emerged as a major technology in the future wireless system to alleviate the worldwide spectrum scarcity issue. Authorized secondary users can take advantages of underutilized spectrum for communication. However, due to the open nature of the wireless medium, the DSA system suffers spectrum misuse by unauthorized secondary users, and thus fewer users would participate in DSA. Although many existing works have implemented misuse detection schemes into DSA, practical concerns, such as channel fading issues, are not well addressed. Therefore, how to ensure the reliable communication among authorized secondary users in a practical channel model becomes a challenging issue. In this paper, we propose CREAM, a physical-layer based misuse detection scheme specifically in the fading environment, which conceals the unforgeable spectrum permit into the message by superposition modulation for verification. Given the pre-shared secret information, the third-party verifier can perform efficient detection on unauthorized spectrum access. Detailed analysis and simulation results demonstrate the security, accuracy, efficiency, and low intrusion to message transmission in fading environments.

The exploding popularity of mobile devices enables people to enjoy benefits brought by various interesting mobile apps. However, the ever-increasing data traffic has exacerbated the congestion on current cellular networks, which results in users' dissatisfaction, especially in crowded areas. Hence, how to alleviate data traffic in cellular networks becomes a challenging problem. Traditional methods rely on mobile offloading techniques to deviate the data traffic originally targeted to cellular networks, such as the small cell, Wi-Fi, and opportunistic communication. Unfortunately, mobile users still experience severe congestion when a large number of users request for data. Facing these challenges, we introduce the concept of mobile participation to assist data offloading by leveraging the mobility of users and the social features among a group of users. A mobile caching user, who pre-caches a certain amount of contents, will roam around congested areas to participate in content dissemination in order to satisfy users' requests, which is expected to benefit both himself and users in the crowd simultaneously. To motivate such human-enabled mobile participation for data offloading, a Stackelberg game is deployed with joint considerations on social effect and delay effect. Based on detailed performance analysis, we demonstrate the feasibility and efficiency of the proposed approach.

The exploding popularity of mobile devices enables people to enjoy benefits brought by various interesting mobile apps, such as social networking, mobile video services, and location-based services, etc. However, the ever-increasing data traffic has exacerbated congestions on current cellular networks, which results in users' dissatisfaction, especially in crowded areas. Hence, how to deal with the explosive data traffic in cellular networks becomes a challenging problem. Traditional methods rely on mobile offloading techniques to deviate the data traffic targeted to cellular networks, such as small cell, Wi-Fi, and opportunistic communication. Unfortunately, mobile users will still experience severe congestion when a large number of users request for data. Facing these challenges, we introduce the concept of mobile participation to assist data offloading by leveraging the mobility of mobile users and the social features among a group of users. A mobile caching user, who pre- caches certain amount of contents, can roam around congested areas to participate in data dissemination in order to satisfy users' requests, which can benefit both herself and users in the crowd simultaneously. Therefore, we propose a game theoretical approach to analyze the data offloading via mobile participation with joint considerations on network effects, congestion, social behaviors, and pricing strategy. Based on detailed performance analysis, we show the feasibility and efficiency of the proposed approach.

In this paper, we propose statistically robust design for multiuser multiple-input multiple-output (MIMO) relay systems with direct source-destination links and imperfect channel state information (CSI). The minimum mean-squared error (MMSE) of the signal waveform estimation at the destination node is adopted as the design criterion. We develop two iterative methods to solve the nonconvex joint source, relay, and receiver optimization problem. Simulation results demonstrate the improved robustness of the proposed algorithms against CSI errors.

This paper investigates the achievable rate of MIMO cognitive radio network when one primary user (PU) and multiple secondary users (SU) are present, where the latter adopt dirty paper coding (DPC) to cancel the interference of PU's transmission at their receivers. Perfect channel state information is assumed at receivers, while only the statistic information of channels are known at the transmitters. We formulate an optimization problem to maximize the achievable rate of the system under the constraints of power limits of each transmitter, where the requirement of not affecting PU's transmission rate is also incorporated. An algorithm is proposed to jointly determine the inflation factors in DPC method and the input covariance matrix of each SU. Simulations show that the proposed problem achieves better achievable rate when compared with the existing results without compromising PU's transmission rate.